The coincidence of a chromosomal fragile site, FRA3B, at a common chromosomal breakpoint in lung cancer has suggested that fragility at this site may predispose to breakage that could contribute to multistep carcinogenesis.
Using a two-stage study design including discovery and replication studies, and stringent Bonferroni correction for multiple statistical analysis, we identified significant genetic interactions between SNPs in <i>RGL1:RAD51B</i> (OR=0.44, <i>p</i> value=3.27x10<sup>-11</sup> in overall lung cancer and OR=0.41, <i>p</i> value=9.71x10<sup>-11</sup> in non-small cell lung cancer), <i>SYNE1:RNF43</i> (OR=0.73, <i>p</i> value=1.01x10<sup>-12</sup> in adenocarcinoma) and <i>FHIT:TSPAN8</i> (OR=1.82, <i>p</i> value=7.62x10<sup>-11</sup> in squamous cell carcinoma) in our analysis.
The purpose of the study was to explore the application of artificial neural network model in the auxiliary diagnosis of lung cancer and compare the effects of back-propagation (BP) neural network with Fisher discrimination model for lung cancer screening by the combined detections of four biomarkers of p16, RASSF1A and FHIT gene promoter methylation levels and the relative telomere length.
These findings suggest FHIT methylation is associated with a higher susceptibility and has a prognostic significance in early stage lung cancer in the Han population of southern-central China and may represent a marker for progressive disease.
Furthermore, abnormal cells were found in 76% sputum by detecting combined HYAL2 and FHIT deletions whereas in 47% sputum by cytology, of the cancer cases, implying that detecting the combination of HYAL2 and FHIT deletions had higher sensitivity than that of sputum cytology for lung cancer diagnosis.